We compare our UAGDet with Mask R-CNN [1]}, [2]}, RoITransformer [3]} and GFNet [4]} models. Specifically, Mask R-CNN [1]} is applied to DOTA [6]} by viewing bounding box annotations as coarse pixel-level annotations and finding minimum bounding boxes based on pixel-level segmentation results [2]}.
RoITransformer (RoITrans) [3]} is based on Faster R-CNN [9]} with one additional FC layers in order to predict rotation parameter \(\theta \)  for rotated bounding boxes. GFNet [4]} uses GNNs to effectively merge dense bounding boxes with a cluster structure, instead of using algorithmic methods such as NMS or Soft-NMS [11]}. Our UAGDet uses GNNs for object graphs composed by objects' uncertainties to improve uncertain object predictions with graph representations, where we use RoITrans as a base object detection network.
